VPDR improves the privacy-utility trade-off in ProtoPFL by allocating less noise to high-variance discriminative prototype dimensions via VPP and using DCR to keep feature norms near the clipping threshold without harming predictions.
Effects of degra- dations on deep neural network architectures
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Proposes JFPD with uncertainty and semantic trust weighting for reliable domain adaptation under distribution shift.
Hidden-Shot adds an implicit visual-task prompt and selective merging step to existing low-level vision generalist models, paired with a 3C4U/3C7U evaluation framework that reports outperformance on seven and ten datasets respectively.
citing papers explorer
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Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
VPDR improves the privacy-utility trade-off in ProtoPFL by allocating less noise to high-variance discriminative prototype dimensions via VPP and using DCR to keep feature norms near the clipping threshold without harming predictions.
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Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation
Proposes JFPD with uncertainty and semantic trust weighting for reliable domain adaptation under distribution shift.
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Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models
Hidden-Shot adds an implicit visual-task prompt and selective merging step to existing low-level vision generalist models, paired with a 3C4U/3C7U evaluation framework that reports outperformance on seven and ten datasets respectively.